{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Training a Classifier\n",
    "=====================\n",
    "\n",
    "This is it. You have seen how to define neural networks, compute loss and make\n",
    "updates to the weights of the network.\n",
    "\n",
    "Now you might be thinking,\n",
    "\n",
    "What about data?\n",
    "----------------\n",
    "\n",
    "Generally, when you have to deal with image, text, audio or video data,\n",
    "you can use standard python packages that load data into a numpy array.\n",
    "Then you can convert this array into a ``torch.*Tensor``.\n",
    "\n",
    "-  For images, packages such as Pillow, OpenCV are useful\n",
    "-  For audio, packages such as scipy and librosa\n",
    "-  For text, either raw Python or Cython based loading, or NLTK and\n",
    "   SpaCy are useful\n",
    "\n",
    "Specifically for vision, we have created a package called\n",
    "``torchvision``, that has data loaders for common datasets such as\n",
    "Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
    "``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
    "\n",
    "This provides a huge convenience and avoids writing boilerplate code.\n",
    "\n",
    "For this tutorial, we will use the CIFAR10 dataset.\n",
    "It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
    "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
    "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
    "\n",
    ".. figure:: /_static/img/cifar10.png\n",
    "   :alt: cifar10\n",
    "\n",
    "   cifar10\n",
    "\n",
    "\n",
    "Training an image classifier\n",
    "----------------------------\n",
    "\n",
    "We will do the following steps in order:\n",
    "\n",
    "1. Load and normalizing the CIFAR10 training and test datasets using\n",
    "   ``torchvision``\n",
    "2. Define a Convolutional Neural Network\n",
    "3. Define a loss function\n",
    "4. Train the network on the training data\n",
    "5. Test the network on the test data\n",
    "\n",
    "1. Loading and normalizing CIFAR10\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "Using ``torchvision``, it’s extremely easy to load CIFAR10.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of torchvision datasets are PILImage images of range [0, 1].\n",
    "We transform them to Tensors of normalized range [-1, 1].\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
    "    the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Widget Javascript not detected.  It may not be installed or enabled properly.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb3e9936ca5d47f8b718718f352c4e23"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us show some of the training images, for fun.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xTYZVHnXiq/kNJcwclsJKZo4TB4lEFY5rIfVOKp0UV2iC1gBduE9vOd69Ys6v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dog  deer  deer   dog\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Define a Convolutional Neural Network\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "Copy the neural network from the Neural Networks section before and modify it to\n",
    "take 3-channel images (instead of 1-channel images as it was defined).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Define a Loss function and optimizer\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Train the network\n",
    "^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "This is when things start to get interesting.\n",
    "We simply have to loop over our data iterator, and feed the inputs to the\n",
    "network and optimize.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.236\n",
      "[1,  4000] loss: 1.846\n",
      "[1,  6000] loss: 1.629\n",
      "[1,  8000] loss: 1.557\n",
      "[1, 10000] loss: 1.516\n",
      "[1, 12000] loss: 1.468\n",
      "[2,  2000] loss: 1.401\n",
      "[2,  4000] loss: 1.384\n",
      "[2,  6000] loss: 1.338\n",
      "[2,  8000] loss: 1.332\n",
      "[2, 10000] loss: 1.296\n",
      "[2, 12000] loss: 1.284\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's quickly save our trained model:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './cifar_net.pth'\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\n",
    "for more details on saving PyTorch models.\n",
    "\n",
    "5. Test the network on the test data\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "We have trained the network for 2 passes over the training dataset.\n",
    "But we need to check if the network has learnt anything at all.\n",
    "\n",
    "We will check this by predicting the class label that the neural network\n",
    "outputs, and checking it against the ground-truth. If the prediction is\n",
    "correct, we add the sample to the list of correct predictions.\n",
    "\n",
    "Okay, first step. Let us display an image from the test set to get familiar.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ynlvnTcKevNz6V+OuldmTFvz9PX6cyd98hX73JbrZmfZbjGi9RrokqIV6bqmf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\nNlrvxSEGNjEY7/ViylzCyU+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W3A9aCP3GtmJ9egfl/OtqpC3ycev68C25/ua9V9bmGM2A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\nHx5wzjQ0TFfCxPlzXqygYDQYJHQDzluJ9rSYEemCwztPv91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5Vc96PM1Mp8rFbkqPYzaN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\nJVzbvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:    cat  ship  ship plane\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load back in our saved model (note: saving and re-loading the model\n",
    "wasn't necessary here, we only did it to illustrate how to do so):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "net.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let us see what the neural network thinks these examples above are:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = net(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outputs are energies for the 10 classes.\n",
    "The higher the energy for a class, the more the network\n",
    "thinks that the image is of the particular class.\n",
    "So, let's get the index of the highest energy:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:    cat   car   car plane\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(outputs, 1)\n",
    "\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results seem pretty good.\n",
    "\n",
    "Let us look at how the network performs on the whole dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 52 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks way better than chance, which is 10% accuracy (randomly picking\n",
    "a class out of 10 classes).\n",
    "Seems like the network learnt something.\n",
    "\n",
    "Hmmm, what are the classes that performed well, and the classes that did\n",
    "not perform well:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of plane : 60 %\n",
      "Accuracy of   car : 66 %\n",
      "Accuracy of  bird : 29 %\n",
      "Accuracy of   cat : 59 %\n",
      "Accuracy of  deer : 57 %\n",
      "Accuracy of   dog : 29 %\n",
      "Accuracy of  frog : 28 %\n",
      "Accuracy of horse : 67 %\n",
      "Accuracy of  ship : 57 %\n",
      "Accuracy of truck : 68 %\n"
     ]
    }
   ],
   "source": [
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        c = (predicted == labels).squeeze()\n",
    "        for i in range(4):\n",
    "            label = labels[i]\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "\n",
    "\n",
    "for i in range(10):\n",
    "    print('Accuracy of %5s : %2d %%' % (\n",
    "        classes[i], 100 * class_correct[i] / class_total[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so what next?\n",
    "\n",
    "How do we run these neural networks on the GPU?\n",
    "\n",
    "Training on GPU\n",
    "----------------\n",
    "Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
    "net onto the GPU.\n",
    "\n",
    "Let's first define our device as the first visible cuda device if we have\n",
    "CUDA available:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of this section assumes that ``device`` is a CUDA device.\n",
    "\n",
    "Then these methods will recursively go over all modules and convert their\n",
    "parameters and buffers to CUDA tensors:\n",
    "\n",
    ".. code:: python\n",
    "\n",
    "    net.to(device)\n",
    "\n",
    "\n",
    "Remember that you will have to send the inputs and targets at every step\n",
    "to the GPU too:\n",
    "\n",
    ".. code:: python\n",
    "\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "\n",
    "Why dont I notice MASSIVE speedup compared to CPU? Because your network\n",
    "is really small.\n",
    "\n",
    "**Exercise:** Try increasing the width of your network (argument 2 of\n",
    "the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\n",
    "they need to be the same number), see what kind of speedup you get.\n",
    "\n",
    "**Goals achieved**:\n",
    "\n",
    "- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
    "- Train a small neural network to classify images\n",
    "\n",
    "Training on multiple GPUs\n",
    "-------------------------\n",
    "If you want to see even more MASSIVE speedup using all of your GPUs,\n",
    "please check out :doc:`data_parallel_tutorial`.\n",
    "\n",
    "Where do I go next?\n",
    "-------------------\n",
    "\n",
    "-  :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n",
    "-  `Train a state-of-the-art ResNet network on imagenet`_\n",
    "-  `Train a face generator using Generative Adversarial Networks`_\n",
    "-  `Train a word-level language model using Recurrent LSTM networks`_\n",
    "-  `More examples`_\n",
    "-  `More tutorials`_\n",
    "-  `Discuss PyTorch on the Forums`_\n",
    "-  `Chat with other users on Slack`_\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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